VMware Tools for Windows update addresses an out of bounds read vulnerability in vm3dmp driver which is installed with vmtools in Windows guest machines. This issue is present in versions 10.2.x and 10.3.x prior to 10.3.10. A local attacker with non-administrative access to a Windows guest with VMware Tools installed may be able to leak kernel information or create a denial of service attack on the same Windows guest machine.

An issue was discovered on Samsung mobile devices with P(9.0) (Exynos chipsets) software. The Wi-Fi kernel drivers have an out-of-bounds Read. The Samsung IDs are SVE-2019-15692, SVE-2019-15693 (December 2019).

In the Linux kernel 5.0.0-rc7 (as distributed in ubuntu/linux.git on kernel.ubuntu.com), mounting a crafted f2fs filesystem image and performing some operations can lead to slab-out-of-bounds read access in ttm_put_pages in drivers/gpu/drm/ttm/ttm_page_alloc.c. This is related to the vmwgfx or ttm module.

An out-of-bounds read in the vrend_blit_need_swizzle function in vrend_renderer.c in virglrenderer through 0.8.0 allows guest OS users to cause a denial of service via VIRGL_CCMD_BLIT commands.

Out of bound read in adm call back function due to incorrect boundary check for payload in command response in Snapdragon Auto, Snapdragon Compute, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables in APQ8053, APQ8098, MDM9206, MDM9207C, MDM9607, MDM9640, MDM9650, MSM8905, MSM8909W, MSM8917, MSM8953, QCS605, SDA660, SDA845, SDM429, SDM429W, SDM439, SDM670, SDM710, SDM845, SDX20, SDX24

Buffer over-read in ADSP parse function due to lack of check for availability of sufficient data payload received in command response in Snapdragon Auto, Snapdragon Compute, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon IoT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables in APQ8009, APQ8053, APQ8098, MDM9206, MDM9207C, MDM9607, MDM9640, MDM9650, MSM8905, MSM8909W, MSM8917, MSM8953, QCS605, SDA660, SDA845, SDM429, SDM429W, SDM439, SDM670, SDM710, SDM845, SDX20, SDX24

Out of bound read in in fingerprint application due to requested data assigned to a local buffer without length check in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wired Infrastructure and Networking in Kamorta, MDM9205, Nicobar, QCS404, QCS405, QCS605, Rennell, SA415M, SA6155P, SC7180, SC8180X, SDM670, SDM710, SDM845, SDM850, SDX24, SDX55, SM6150, SM7150, SM8150, SM8250, SXR1130, SXR2130

When attempting to create a new XFRM policy, a stack out-of-bounds read will occur if the user provides a template where the mode is set to a value that does not resolve to a valid XFRM mode in Snapdragon Auto, Snapdragon Compute, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon IoT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables, Snapdragon Wired Infrastructure and Networking in APQ8009, APQ8053, APQ8096AU, APQ8098, IPQ4019, IPQ8074, MDM9206, MDM9207C, MDM9607, MDM9640, MDM9650, MSM8905, MSM8909W, MSM8917, MSM8953, MSM8996AU, QCA4531, QCN7605, QCS605, QM215, SA415M, SC8180X, SDA660, SDA845, SDM429, SDM429W, SDM439, SDM450, SDM630, SDM632, SDM636, SDM660, SDM845, SDX20, SDX24, SDX55, SM6150, SM7150, SM8150, SM8250, SXR2130

Out of bounds read can happen in diag event set mask command handler when user provided length in the command request is less than expected length in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon IoT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables, Snapdragon Wired Infrastructure and Networking in APQ8009, APQ8096, APQ8096AU, APQ8098, Kamorta, MDM9150, MDM9205, MDM9206, MDM9607, MDM9625, MDM9635M, MDM9640, MDM9650, MDM9655, MSM8905, MSM8909, MSM8909W, MSM8917, MSM8920, MSM8937, MSM8940, MSM8953, MSM8996, MSM8996AU, MSM8998, Nicobar, QCM2150, QCN7605, QCS404, QCS405, QCS605, QM215, Rennell, SA415M, Saipan, SC7180, SC8180X, SDA660, SDA845, SDM429, SDM429W, SDM439, SDM450, SDM630, SDM632, SDM636, SDM660, SDM670, SDM710, SDM845, SDM850, SDX24, SDX55, SM6150, SM7150, SM8150, SXR1130

Out of bound read in Fingerprint application due to requested data is being used without length check in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wired Infrastructure and Networking in Kamorta, MDM9150, MDM9205, MDM9650, MSM8998, Nicobar, QCS404, QCS405, QCS605, Rennell, SA415M, SA6155P, SC7180, SC8180X, SDA660, SDM630, SDM636, SDM660, SDM670, SDM710, SDM845, SDM850, SDX24, SDX55, SM6150, SM7150, SM8150, SM8250, SXR1130, SXR2130

Out of bound access in diag services when DCI command buffer reallocation is not done properly with required capacity in Snapdragon Auto, Snapdragon Compute, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Wearables in APQ8009, APQ8096AU, MDM9206, MDM9207C, MDM9607, MDM9640, MDM9650, QCS605, Rennell, SC8180X, SDM429W, SDM710, SDX55, SM7150, SM8150

Out of bound memory access can happen while parsing ADSP message due to lack of check of size of payload received from userspace in Snapdragon Auto, Snapdragon Compute, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Wired Infrastructure and Networking in APQ8009, APQ8096AU, IPQ4019, IPQ6018, IPQ8064, IPQ8074, MDM9206, MDM9207C, MDM9607, MDM9640, MDM9650, QCN7605, QCS605, SC8180X, SDM710, SDX24, SDX55, SM8150, SM8250, SXR2130

Lack of boundary checks for data offsets received from HLOS can lead to out-of-bound read in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon IoT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables, Snapdragon Wired Infrastructure and Networking in APQ8009, APQ8016, APQ8017, APQ8053, APQ8076, APQ8096, APQ8096AU, APQ8098, MDM9150, MDM9205, MDM9206, MDM9207C, MDM9607, MDM9640, MDM9650, MDM9655, MSM8905, MSM8909, MSM8909W, MSM8917, MSM8920, MSM8937, MSM8940, MSM8953, MSM8996, MSM8996AU, MSM8998, QCM2150, QCS605, QM215, Rennell, SC7180, SDA660, SDA845, SDM429, SDM429W, SDM439, SDM450, SDM630, SDM632, SDM636, SDM660, SDM670, SDM710, SDM845, SDM850, SM6150, SM7150, SM8150, SXR1130, SXR2130

In Android releases from CAF using the linux kernel (Android for MSM, Firefox OS for MSM, QRD Android) before security patch level 2018-06-05, kernel panic may happen due to out-of-bound read, caused by not checking source buffer length against length of packet stream to be copied.

An out-of-bounds read was addressed with improved input validation. This issue affected versions prior to macOS Mojave 10.14.2.

Buffer Over-read at parse_rawml.c:1416 in GitHub repository bfabiszewski/libmobi prior to 0.11. The bug causes the program reads data past the end of the intented buffer. Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash.

In all android releases (Android for MSM, Firefox OS for MSM, QRD Android) from CAF using the linux kernel, Venus HW searches for start code when decoding input bit stream buffers. If start code is not found in entire buffer, there is over-fetch beyond allocation length. This leads to page fault.

TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for `QuantizeV2` can trigger a read outside of bounds of heap allocated array. This occurs whenever `axis` is a negative value less than `-1`. In this case, we are accessing data before the start of a heap buffer. The code allows `axis` to be an optional argument (`s` would contain an `error::NOT_FOUND` error code). Otherwise, it assumes that `axis` is a valid index into the dimensions of the `input` tensor. If `axis` is less than `-1` then this results in a heap OOB read. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, as this version is the only one that is also affected.

TensorFlow is an open source platform for machine learning. In affected versions the implementation of `FusedBatchNorm` kernels is vulnerable to a heap OOB access. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. In affected versions the implementation of `SparseBinCount` is vulnerable to a heap OOB access. This is because of missing validation between the elements of the `values` argument and the shape of the sparse output. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. In affected versions the shape inference functions for `SparseCountSparseOutput` can trigger a read outside of bounds of heap allocated array. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

NVIDIA TrustZone Software contains a vulnerability in the Keymaster implementation where the software reads data past the end, or before the beginning, of the intended buffer; and may lead to denial of service or information disclosure. This issue is rated as high.

The TCP stack in the Linux kernel through 4.10.6 mishandles the SCM_TIMESTAMPING_OPT_STATS feature, which allows local users to obtain sensitive information from the kernel's internal socket data structures or cause a denial of service (out-of-bounds read) via crafted system calls, related to net/core/skbuff.c and net/socket.c.

VMware Workstation (14.x before 14.1.0 and 12.x) and Horizon View Client (4.x before 4.7.0) contain an out-of-bounds read vulnerability in TPView.dll. On Workstation, this issue in conjunction with other bugs may allow a guest to leak information from host or may allow for a Denial of Service on the Windows OS that runs Workstation. In the case of a Horizon View Client, this issue in conjunction with other bugs may allow a View desktop to leak information from host or may allow for a Denial of Service on the Windows OS that runs the Horizon View Client. Exploitation is only possible if virtual printing has been enabled. This feature is not enabled by default on Workstation but it is enabled by default on Horizon View.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to `BoostedTreesSparseCalculateBestFeatureSplit`. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/stats_ops.cc) needs to validate that each value in `stats_summary_indices` is in range. We have patched the issue in GitHub commit e84c975313e8e8e38bb2ea118196369c45c51378. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of sparse reduction operations in TensorFlow can trigger accesses outside of bounds of heap allocated data. The [implementation](https://github.com/tensorflow/tensorflow/blob/a1bc56203f21a5a4995311825ffaba7a670d7747/tensorflow/core/kernels/sparse_reduce_op.cc#L217-L228) fails to validate that each reduction group does not overflow and that each corresponding index does not point to outside the bounds of the input tensor. We have patched the issue in GitHub commit 87158f43f05f2720a374f3e6d22a7aaa3a33f750. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

Possible buffer over read due to improper buffer allocation for file length passed from user space in Snapdragon Auto, Snapdragon Connectivity, Snapdragon Industrial IOT, Snapdragon Mobile

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation(https://github.com/tensorflow/tensorflow/blob/ef0c008ee84bad91ec6725ddc42091e19a30cf0e/tensorflow/core/kernels/maxpooling_op.cc#L1016-L1017) uses the same value to index in two different arrays but there is no guarantee that the sizes are identical. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can read data outside of bounds of heap allocated buffer in `tf.raw_ops.QuantizeAndDequantizeV3`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/11ff7f80667e6490d7b5174aa6bf5e01886e770f/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L237) does not validate the value of user supplied `axis` attribute before using it to index in the array backing the `input` argument. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

TensorFlow is an end-to-end open source platform for machine learning. Incomplete validation in `tf.raw_ops.CTCLoss` allows an attacker to trigger an OOB read from heap. The fix will be included in TensorFlow 2.5.0. We will also cherrypick these commits on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

Lack of boundary checking of a buffer in libSPenBase library of Samsung Notes prior to Samsung Note version 4.3.02.61 allows OOB read.

Possible out of bounds read due to incorrect validation of incoming buffer length in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile

Possible buffer over read due to lack of data length check in QVR Service configuration in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Wearables

An issue was discovered in the Linux kernel 3.16 through 5.5.6. set_fdc in drivers/block/floppy.c leads to a wait_til_ready out-of-bounds read because the FDC index is not checked for errors before assigning it, aka CID-2e90ca68b0d2.

VMware ESXi (6.7 before ESXi670-202006401-SG and 6.5 before ESXi650-202005401-SG), Workstation (15.x before 15.5.5), and Fusion (11.x before 11.5.5) contain an out-of-bounds read vulnerability in NVMe functionality. A malicious actor with local non-administrative access to a virtual machine with a virtual NVMe controller present may be able to read privileged information contained in physical memory.